Autoencoders have recently become popular for the nonlinear dimensionality reduction of datasets extracted from several high dimensional systems. These have been motivated by the extraction of coherent structures that parameterize low-dimensional embeddings in manifolds [1]}, [2]}, [3]}, and the utilization of these embeddings for efficient surrogate models of nonlinear dynamical systems [4]}, [5]}, [6]}, [7]}, [8]}. In this work, we utilize convolutional autoencoders to identify low-dimensional representations of experimentally collected data for building parameter-observation maps where the former are obtained through meteorological and wind turbine data and the latter are LiDAR measurements collected in the wake generated by wind turbines.
